Abstract: Quaternion-valued neural networks extend real-valued neural networks to the algebra of quaternions. Quaternion-valued neural networks achieve higher accuracy or faster convergence than real-valued neural networks in some tasks, such as singular point compensation in polarimetric synthetic aperture, spoken language understanding, and radar robot control. The performance of quaternion-valued neural networks is widely supported by empirical studies, but there are few studies about theoretical properties of quaternion-valued neural networks, especially why quaternion-valued neural networks can be more efficient than real-valued neural networks. In this paper, we investigate theoretical properties of quaternion-valued neural networks and the advantages of quaternion-valued neural networks compared with real-valued neural networks from the perspective of approximation. Firstly, we prove the universal approximation of quaternion-valued neural networks with a non-split ReLU (rectified linear unit)-type activation function. Secondly, we demonstrate the approximation advantages of quaternion-valued neural networks compared with real-valued neural networks. For split ReLU-type activation functions, we show that one-hidden-layer real-valued neural networks need about 4 times the number of parameters to possess the same maximum number of convex linear regions as one-hidden-layer quaternion-valued neural networks. For the non-split ReLU-type activation function, we prove the approximation separation between one-hidden-layer quaternion-valued neural networks and one-hidden-layer real-valued neural networks, i.e., a quaternion-valued neural network can express a real-valued neural network using the same number of hidden neurons and the same parameter norm, while a real-valued neural network cannot approximate a quaternion-valued neural network unless the number of hidden neurons is exponentially large or the parameters are exponentially large. Finally, simulation experiments support our theoretical findings.